“The emerging AI community on HPC infrastructure is critical to achieving the vision of AI,” said Pradeep Dubey, Intel Fellow. “Machines that don’t just crunch numbers, but help us make better and more informed complex decisions. Scalability is the key to AI-HPC so scientists can address the big compute, big data challenges facing them and to make sense from the wealth of measured and modeled or simulated data that is now available to them.”

New workloads in machine learning and analytics have placed significant burdens on traditional HPC systems. This guest post explores how HPE, Intel and WekaIO are working together to solve potential I/O bottlenecks in machine learning and AI workloads.

Today Dell EMC announced new Ready Solutions for AI. With specialized designs for Machine Learning with Hadoop and Deep Learning with NVIDIA, the Dell EMC Ready Solutions simplify AI environments, deliver faster, deeper insights than the competition1, and leverage Dell EMC’s proven expertise to help organizations realize the full potential of AI. “What we’re announcing today allows customers at any scale to start seeing better business outcomes and positions them for AI’s increasingly important role in the future.”

SC18 is starting their series of Invited Talk Previews this week with AI luminary Bryan Catanzaro. Now at NVIDIA, Catanzaro is one of the most respected (and entertaining) speakers in the field of Machine Learning. “I will discuss how we go about applying deep learning to our work at NVIDIA, solving problems in a variety of domains from graphics and vision to text and speech. I’ll discuss the properties of successful machine learning applications, the criteria that we use to choose projects, and things to watch out for while creating new deep learning applications. I’ll discuss the role of HPC in helping us conduct our research, and I’ll show some examples of projects that benefit from scale.”

According to a new Ovum white paper, sponsored by NVIDIA, there is a huge opportunity to help physicians with AI-based systems that can cut down on the workload across protocoling, imaging analysis, and automated reporting of the results.

“Dell EMC Ready Solutions for AI are validated, hardware and software stacks optimized to accelerate AI initiatives, shortening deployment time from weeks to days. They increase data scientist productivity by offering self‐service workspaces, allowing each data scientist to configure their environment from a library of AI models and frameworks in just five clicks. Customers report that Dell EMC Hadoop solutions can help boost data scientist productivity by as much as 30 percent. IT operations are also simplified through a single console for monitoring the health and configuration of the cluster.”

This report delves into many advances in clinical imaging being introduced through AI. The activity today is mostly focused on working with the current computational environment that exists in the radiologist’s laboratory, and examines how advanced medical instruments and software solutions that incorporate AI can augment a radiologist’s work. Download the new white paper from NVIDIA that explores increasing productivity with AI and how tools like deep learning can enhance offerings and cut down on cost.

At the recent NVIDIA GPU Technology Conference (GTC) 2018, Jensen Huang, NVIDIA President and CEO, during his presentation focused on a new framework designed to contextualize the key challenges using AI systems and delivering deep learning-based solutions. A new white paper sponsored by NVIDIA outlines these requirements — coined PLASTER.

In this video, Steve Conway from Hyperion Research provides a summary of the ISC High Performance Conference in Frankfurt, Germany. “The ISC High Performance conference attracted 3,505 attendees from over 60 countries, as well as 162 companies and research organizations to unveil their latest technologies and services at the event. With this feat, ISC High Performance has officially reached a new milestone in its 33-year conference history.”

The is the final entry in a five-part insideHPC series that takes an in-depth look at how machine learning, deep learning and AI are being used in the energy industry. Read on for help determining where and how to adopt machine learning technology in your business.

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Industry Perspectives

In this special guest post, Axel Huebl looks at the TOP500 and HPCG with an eye on power efficiency trends to watch on the road to Exascale. "This post will focus one efficiency, in terms of performance per Watt, simply because system power envelope is a major constrain for upcoming Exascale systems. With the great numbers from TOP500, we try to extend theoretical estimates from theoretical Flop/Ws of individual compute hardware to system scale." [Read More...]

White Papers

For this report, DDN performed a number of experimental benchmarks to attain optimal IO rates for Paradigm Echos application workloads. It present results from IO intensive Echos micro-benchmarks to illustrate the DDN GRIDScaler performance benefits and provide some detail to aid optimal job packing in 40G Ethernet clusters. To find out the results download this guide.